Increasingly complex features have been implemented on mobile devices, such as mobile phones. Current mobile devices provide access to a variety of information through web interfaces and graphical user interface displays, but the user is typically limited to using a keypad to navigate through a menu hierarchy to select a desired application.
In addition to graphical user interfaces, there have been a number of voice-enabled user interfaces. Short Message Service (SMS) dictation, email dictation, name dialing applications, dialogue driven calendar applications, and music player applications can interactively guide a user through a task. Conventional spoken dialogue systems ask a user a series of fixed questions in a fixed order to narrow a field of possible answers. To find a restaurant, for instance, the system could prompt a user to specify preferences for cuisine, neighborhood, and price range, etc., before providing any answers. This type of interaction fails to address the information seeking needs of users who do not have well-defined preferences, or who may wish to explore the space of possibilities.
Conventional algorithms have also been developed that model dialogue as a Markov Decision Process and optimize the model via reinforcement learning. These algorithms, however, rely on complex and costly training data derived from large numbers of human-machine interactions or simulations of such dialogues.